prompt stringlengths 94 42.6k | completion stringlengths 6 120 | api stringlengths 14 68 |
|---|---|---|
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | GraphInference(modified_model) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor([1, 2]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor([3, 4]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.add(varo, inp_c) | megengine.functional.add |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | GraphInference(modified_model) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor([1.0, 2.0]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor([3.0, 4.0]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.sigmoid(var_a + var_b) | megengine.functional.sigmoid |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | GraphInference(modified_model) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | G.load_graph(optimize_model) | megengine.core.tensor.megbrain_graph.load_graph |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(modified_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(modified_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor([1.0, 2.0]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.cond_take(var_a > 1, var_a) | megengine.functional.cond_take |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | GraphInference(modified_model) | megengine.utils.comp_graph_tools.GraphInference |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor([1.0, 2.0]) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | trace(symbolic=True, capture_as_const=True) | megengine.jit.tracing.trace |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Net.load(orig_model) | megengine.utils.network.Network.load |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | set_symbolic_shape(True) | megengine.utils.network.set_symbolic_shape |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | set_symbolic_shape(False) | megengine.utils.network.set_symbolic_shape |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | set_symbolic_shape(saved_symbolic_shape) | megengine.utils.network.set_symbolic_shape |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.exp(x) | megengine.functional.exp |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | Tensor(5.0) | megengine.tensor.Tensor |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.exp(x) | megengine.functional.exp |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.exp(x) | megengine.functional.exp |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.cond_take(a > 1, a) | megengine.functional.cond_take |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | F.relu(a * 2) | megengine.functional.relu |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | M.Conv2d(3, 32, 3) | megengine.module.Conv2d |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | M.Conv2d(32, 32, 3) | megengine.module.Conv2d |
import io
import numpy as np
import megengine.core.tensor.megbrain_graph as G
import megengine.functional as F
import megengine.module as M
import megengine.utils.network_node as N
from megengine.jit.tracing import trace
from megengine.tensor import Tensor
from megengine.utils.comp_graph_tools import GraphInference
f... | M.Conv2d(32, 32, 3) | megengine.module.Conv2d |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.zeros((d_model, *max_shape)) | megengine.functional.zeros |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.expand_dims(div_term, (1, 2)) | megengine.functional.expand_dims |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.sin(x_position * div_term) | megengine.functional.sin |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.cos(x_position * div_term) | megengine.functional.cos |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.sin(y_position * div_term) | megengine.functional.sin |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.cos(y_position * div_term) | megengine.functional.cos |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.expand_dims(pe, 0) | megengine.functional.expand_dims |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.ones(max_shape) | megengine.functional.ones |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.ones(max_shape) | megengine.functional.ones |
import math
import megengine.module as M
import megengine.functional as F
class PositionEncodingSine(M.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (t... | F.arange(0, d_model // 2, 2) | megengine.functional.arange |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import cv2
import megengine.functional as F
import numpy as np
__all__ = [
"preprocess",
"postprocess",
]
def preprocess(image, input_size, mean, std, swap=(2, 0, 1)):
if len(image.sha... | F.zeros_like(prediction) | megengine.functional.zeros_like |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import cv2
import megengine.functional as F
import numpy as np
__all__ = [
"preprocess",
"postprocess",
]
def preprocess(image, input_size, mean, std, swap=(2, 0, 1)):
if len(image.sha... | F.squeeze(class_conf) | megengine.functional.squeeze |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import cv2
import megengine.functional as F
import numpy as np
__all__ = [
"preprocess",
"postprocess",
]
def preprocess(image, input_size, mean, std, swap=(2, 0, 1)):
if len(image.sha... | F.concat((image_pred[:, :5], class_conf, class_pred), 1) | megengine.functional.concat |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import cv2
import megengine.functional as F
import numpy as np
__all__ = [
"preprocess",
"postprocess",
]
def preprocess(image, input_size, mean, std, swap=(2, 0, 1)):
if len(image.sha... | F.concat((output[i], detections)) | megengine.functional.concat |
import argparse
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine.core._imperative_rt import make_h2d
def change_batch_and_dump(inp_file, oup_file):
cg, _, outputs = | G.load_graph(inp_file) | megengine.core.tensor.megbrain_graph.load_graph |
import argparse
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine.core._imperative_rt import make_h2d
def change_batch_and_dump(inp_file, oup_file):
cg, _, outputs = G.load_graph(inp_file)
inputs = | cgtools.get_dep_vars(outputs[0], "Host2DeviceCopy") | megengine.utils.comp_graph_tools.get_dep_vars |
import argparse
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine.core._imperative_rt import make_h2d
def change_batch_and_dump(inp_file, oup_file):
cg, _, outputs = G.load_graph(inp_file)
inputs = cgtools.get_dep_vars(outputs[0], "Host2DeviceC... | cgtools.replace_vars(outputs, replace_dict) | megengine.utils.comp_graph_tools.replace_vars |
import argparse
import megengine.core.tensor.megbrain_graph as G
import megengine.utils.comp_graph_tools as cgtools
from megengine.core._imperative_rt import make_h2d
def change_batch_and_dump(inp_file, oup_file):
cg, _, outputs = G.load_graph(inp_file)
inputs = cgtools.get_dep_vars(outputs[0], "Host2DeviceC... | make_h2d(cg, "xpux", var.dtype, n_shape, var.name) | megengine.core._imperative_rt.make_h2d |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.load_comp_graph_from_file(src_model_file) | megengine._internal.load_comp_graph_from_file |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.graph_traversal(outputs) | megengine._internal.cgtools.graph_traversal |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.get_oprs_seq(outputs, prune_reshape=True) | megengine._internal.cgtools.get_oprs_seq |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.get_opr_type(mge_op) | megengine._internal.cgtools.get_opr_type |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.get_opr_type(mge_op) | megengine._internal.cgtools.get_opr_type |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.get_opr_type(mge_op) | megengine._internal.cgtools.get_opr_type |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.get_opr_type(consumer_op) | megengine._internal.cgtools.get_opr_type |
import os
import math
import numpy as np
import six
import megengine._internal as mgb
from enum import Enum
from py_proto import mace_pb2
from transform import base_converter
from transform.base_converter import PoolingType
from transform.base_converter import ActivationType
from transform.base_converter import Eltwis... | mgb.cgtools.get_opr_type(next_op) | megengine._internal.cgtools.get_opr_type |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | M.Linear(64, 9) | megengine.module.Linear |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.warp_perspective(image, mat3x3, [s, s]) | megengine.functional.warp_perspective |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.avg_pool2d(x, 7) | megengine.functional.avg_pool2d |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.flatten(x, 1) | megengine.functional.flatten |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.broadcast_to(base, residual.shape) | megengine.functional.broadcast_to |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.broadcast_to(left_scale, residual.shape) | megengine.functional.broadcast_to |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.broadcast_to(right_scale, residual.shape) | megengine.functional.broadcast_to |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | M.Conv2d(3, 8, kernel_size=3, stride=2, padding=1, bias=False) | megengine.module.Conv2d |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | M.BatchNorm2d(8) | megengine.module.BatchNorm2d |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | M.ReLU() | megengine.module.ReLU |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | M.MaxPool2d(kernel_size=2, stride=2) | megengine.module.MaxPool2d |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | F.matmul(base + residual, right_scale) | megengine.functional.matmul |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | mge.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) | megengine.tensor |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | mge.tensor([[s, 0, 0], [0, s, 0], [0, 0, 1]]) | megengine.tensor |
# Copyright (c) Megvii, Inc. and its affiliates.
import megengine as mge
import megengine.functional as F
import megengine.module as M
from .resnet import BasicBlock
class STN(M.Module):
"""spatial transformer networks from
`"Spatial Transformer Networks" <https://arxiv.org/pdf/1506.02025.pdf>`_
some de... | mge.tensor([[1 / s, 0, 0], [0, 1 / s, 0], [0, 0, 1]]) | megengine.tensor |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | F.sum(b) | megengine.functional.sum |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | F.grad(c, x, use_virtual_grad=False) | megengine.functional.grad |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | F.grad(c, y, use_virtual_grad=False) | megengine.functional.grad |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | F.grad(c, z, use_virtual_grad=False) | megengine.functional.grad |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | M.BatchNorm2d(4) | megengine.module.BatchNorm2d |
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTI... | M.BatchNorm2d(4, affine=False) | megengine.module.BatchNorm2d |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.nn.pad(x2, ((0, 0), (0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size))) | megengine.functional.nn.pad |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.concat(cv, 1) | megengine.functional.concat |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | nn.LeakyReLU(0.1) | megengine.module.LeakyReLU |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.concat((x1, x2), axis=1) | megengine.functional.concat |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.concat((x1, x2), axis=1) | megengine.functional.concat |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.sigmoid(out) | megengine.functional.sigmoid |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.zeros((batch_size, 2, h_x1, w_x1), dtype=dtype) | megengine.functional.zeros |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | nn.LeakyReLU(0.1) | megengine.module.LeakyReLU |
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
# (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, ... | F.concat([x1, x2], axis=1) | megengine.functional.concat |
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